Detecting defense mechanisms from Adult Attachment Interview (AAI) transcripts using machine learning.
Psychother Res
; 33(6): 757-767, 2023 07.
Article
en En
| MEDLINE
| ID: mdl-36525586
OBJECTIVE: Defensive functioning (i.e., unconscious process used to manage real or perceived threats) may play a role in the development of various psychopathologies. It is typically assessed via observer rating measures, however, human coding of defensive functioning is resource-intensive and time-consuming. The purpose of this study was to develop a machine learning approach to automate coding of defense mechanisms from interview transcripts. METHOD: Participants included a clinical sample of women with binge-eating disorder (n = 92) and a community sample without binge-eating disorder (n = 66). We trained and evaluated five RoBERTa-based models to detect the presence of defenses in 16,785 interviewer-participant talk-turn pairs nested within 192 interviews. A model detected the presence of any defense, while four additional models detected the most common defenses in this sample (repression, intellectualization, reaction formation, undoing). RESULTS: The models were capable of distinguishing defenses (ROC-AUC .82-.90) but were not proficient enough to warrant replacing human coders (PR-AUC .28-.60). Follow-up analysis was performed to assess other practical uses of these models. DISCUSSION: Our machine learning models could be used to assist coders. Future research should conduct a deployment study to determine if human coding of defense mechanisms can be expedited using machine learning models.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Mecanismos de Defensa
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Aprendizaje Automático
Tipo de estudio:
Qualitative_research
Límite:
Adult
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Female
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Humans
Idioma:
En
Revista:
Psychother Res
Asunto de la revista:
PSICOLOGIA
/
PSIQUIATRIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Canadá